LLM-Enhanced Fuzzing Uncovers 100+ Compiler Bugs in Smart Contract Languages

A novel approach combining coverage-guided fuzzing with grammar-awareness and LLM assistance has successfully identified over 100 compiler bugs in prominent smart contract languages, including Sui Move, Cairo, Solang, Solidity, and Leo. This is a critical development because these bugs were triggered by structurally valid programs against mature, audited production compilers, indicating a deeper class of vulnerabilities than typical lexer or parser errors. The ability to uncover such flaws in high-stakes environments like smart contracts directly impacts the security and reliability of blockchain ecosystems, where even minor compiler errors can have catastrophic financial consequences. This advancement signifies a crucial step in bolstering the integrity of the digital infrastructure underpinning decentralized finance and other critical applications.

Traditional fuzzing, operating at byte or bit levels, is largely ineffective for compilers due to their inherent requirement for structured input, often leading only to superficial lexer or parser errors. This limitation necessitated the evolution towards grammar-aware fuzzing techniques. The new methodology significantly enhances grammar-aware fuzzing by leveraging LLMs to generate more sophisticated and challenging test cases. By understanding language structures, LLMs can craft inputs that push past basic syntax checks to probe deeper compiler passes—such as typechecking, semantic analysis, and code generation—where complex invariants and assumptions reside. The focus was specifically on Internal Compiler Errors (ICE), which, while not directly affecting running programs, can disrupt development workflows and indicate underlying compiler weaknesses that could lead to more severe vulnerabilities. The discovery of numerous bugs in systems previously considered robust highlights the limitations of prior testing paradigms and the power of AI-driven generation.

The success of LLM-enhanced fuzzing signals a paradigm shift in compiler testing and, by extension, software quality assurance for complex, structured inputs. This approach offers a robust blueprint for improving the security posture of critical infrastructure, particularly in rapidly evolving domains like blockchain and AI-driven systems where code integrity is paramount. As programming languages become more intricate and their compilers more complex, integrating advanced AI capabilities into testing frameworks will become indispensable for maintaining integrity and preventing costly exploits. This methodology could lead to a new generation of highly resilient compilers, fostering greater confidence in the foundational software layers that underpin digital economies and accelerating the secure development of future AI applications.